Publication: Performance investigation of starfive visionfive2 RISC-V board for object detection
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering::Electrical and electronic engineering | |
| dc.contributor.author | Nasrul Naim bin Mohd Yaakub | |
| dc.date.accessioned | 2025-05-20T07:42:18Z | |
| dc.date.available | 2025-05-20T07:42:18Z | |
| dc.date.issued | 2024-08 | |
| dc.description.abstract | This research is dedicated to a thorough comparison and evaluation of the VisionFive V2 Board's performance relative to the current market leader, the Raspberry Pi model. Additionally, it investigates the processing speed of the VisionFive2 RISC-V soft processor in the context of object detection tasks. The focus is on deploying the advanced YOLOv5 object detection algorithm on the VisionFive2 platform and assessing its efficacy. A detailed analysis of various performance metrics, including accuracy and processing speed, is conducted to understand the strengths and limitations of the VisionFive V2 Board. This research aims to determine the feasibility and effectiveness of the VisionFive2 platform for real-time object detection applications by benchmarking its performance against the well-established Raspberry Pi model. Through this comprehensive study, the findings will offer valuable insights into the potential of VisionFive V2 in various practical applications, particularly in the field of embedded systems and IoT. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/21749 | |
| dc.language.iso | en | |
| dc.title | Performance investigation of starfive visionfive2 RISC-V board for object detection | |
| dc.type | Resource Types::text::report::technical report | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |